YOLO-ELD: Efficient and Lightweight Detection for UAV Aerial Imagery

Object detection in UAV imagery has become a hot topic in recent years. However, deploying real-time detection models on UAV platforms is highly challenging due to limited computational power and memory. Moreover, the large size of UAV-captured images, the small size of objects, and their dense distribution all impact detection efficiency. Many researchers have made a series of improvements to address these issues, but they have not maintained a good balance among model size, inference speed, and accuracy. To address above difficulties, this paper proposes an efficient and lightweight model that maintains moderate detection accuracy while achieving model lightweighting and reduced inference time. Concretely, considering the higher resolution of drone-captured images, we have designed a backbone with more lightweight downsampling modules, enhancing deployment efficiency on devices with limited resources. Additionally, this work incorporates a self-attention mechanism in the feature extraction component of the model, which significantly improves the ability to process critical areas in the image, crucial for detecting small-scale and densely distributed targets. Moreover, this work designs an IoU tailored for drone aerial images, which calculates losses by focusing on the shape and scale of bounding boxes, thereby enhancing the accuracy of bounding box regression. Additionally, it uses a ratio of scale factors to control the generation of auxiliary bounding boxes, which aids in loss calculation and accelerates convergence. Experimental results on the VisDrone 2019 dataset show that compared to existing detection methods used for drones, our model is more lightweight and efficient, while also achieving medium accuracy. Also, compared to our baseline method, YOLO-ELD reduces the number of model parameters by about 40%, increases the inference speed of the model by 10%, and also improves the precision of model by 3%.